Overview

Dataset statistics

Number of variables15
Number of observations1000
Missing cells634
Missing cells (%)4.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory117.3 KiB
Average record size in memory120.1 B

Variable types

Numeric8
Categorical7

Alerts

join_date has constant value "54:25.3"Constant
age has 53 (5.3%) missing valuesMissing
gender has 48 (4.8%) missing valuesMissing
region has 52 (5.2%) missing valuesMissing
education_level has 51 (5.1%) missing valuesMissing
employment_type has 54 (5.4%) missing valuesMissing
annual_income has 50 (5.0%) missing valuesMissing
loan_amount has 44 (4.4%) missing valuesMissing
loan_purpose has 37 (3.7%) missing valuesMissing
credit_score has 45 (4.5%) missing valuesMissing
repayment_history has 46 (4.6%) missing valuesMissing
transaction_count has 49 (4.9%) missing valuesMissing
spending_ratio has 49 (4.9%) missing valuesMissing
join_date has 56 (5.6%) missing valuesMissing
customer_id is uniformly distributedUniform
customer_id has unique valuesUnique
repayment_history has 71 (7.1%) zerosZeros

Reproduction

Analysis started2026-02-17 10:46:40.917489
Analysis finished2026-02-17 10:46:59.810053
Duration18.89 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Uniform  Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1500.5
Minimum1001
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-17T16:17:00.313778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1050.95
Q11250.75
median1500.5
Q31750.25
95-th percentile1950.05
Maximum2000
Range999
Interquartile range (IQR)499.5

Descriptive statistics

Standard deviation288.81944
Coefficient of variation (CV)0.19248213
Kurtosis-1.2
Mean1500.5
Median Absolute Deviation (MAD)250
Skewness0
Sum1500500
Variance83416.667
MonotonicityStrictly increasing
2026-02-17T16:17:00.433075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.1%
10021
 
0.1%
10031
 
0.1%
10041
 
0.1%
10051
 
0.1%
10061
 
0.1%
10071
 
0.1%
10081
 
0.1%
10091
 
0.1%
10101
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
10011
0.1%
10021
0.1%
10031
0.1%
10041
0.1%
10051
0.1%
10061
0.1%
10071
0.1%
10081
0.1%
10091
0.1%
10101
0.1%
ValueCountFrequency (%)
20001
0.1%
19991
0.1%
19981
0.1%
19971
0.1%
19961
0.1%
19951
0.1%
19941
0.1%
19931
0.1%
19921
0.1%
19911
0.1%

age
Real number (ℝ)

Missing 

Distinct52
Distinct (%)5.5%
Missing53
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean43.726505
Minimum18
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-17T16:17:00.575054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q131
median44
Q356
95-th percentile67
Maximum69
Range51
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.997539
Coefficient of variation (CV)0.34298509
Kurtosis-1.1368642
Mean43.726505
Median Absolute Deviation (MAD)12
Skewness-0.027393374
Sum41409
Variance224.92618
MonotonicityNot monotonic
2026-02-17T16:17:00.769836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4328
 
2.8%
5227
 
2.7%
5026
 
2.6%
4526
 
2.6%
6625
 
2.5%
5422
 
2.2%
2222
 
2.2%
6822
 
2.2%
4022
 
2.2%
1921
 
2.1%
Other values (42)706
70.6%
(Missing)53
 
5.3%
ValueCountFrequency (%)
1821
2.1%
1921
2.1%
2019
1.9%
2115
1.5%
2222
2.2%
2315
1.5%
2414
1.4%
2520
2.0%
2617
1.7%
2714
1.4%
ValueCountFrequency (%)
6921
2.1%
6822
2.2%
6715
1.5%
6625
2.5%
6519
1.9%
6420
2.0%
6310
 
1.0%
6221
2.1%
6117
1.7%
6011
1.1%

gender
Categorical

Missing 

Distinct3
Distinct (%)0.3%
Missing48
Missing (%)4.8%
Memory size7.9 KiB
Male
325 
Female
317 
Other
310 

Length

Max length6
Median length5
Mean length4.9915966
Min length4

Characters and Unicode

Total characters4752
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowOther
3rd rowOther
4th rowMale
5th rowOther

Common Values

ValueCountFrequency (%)
Male325
32.5%
Female317
31.7%
Other310
31.0%
(Missing)48
 
4.8%

Length

2026-02-17T16:17:01.116400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-17T16:17:01.197782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male325
34.1%
female317
33.3%
other310
32.6%

Most occurring characters

ValueCountFrequency (%)
e1269
26.7%
a642
13.5%
l642
13.5%
M325
 
6.8%
F317
 
6.7%
m317
 
6.7%
O310
 
6.5%
t310
 
6.5%
h310
 
6.5%
r310
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)4752
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1269
26.7%
a642
13.5%
l642
13.5%
M325
 
6.8%
F317
 
6.7%
m317
 
6.7%
O310
 
6.5%
t310
 
6.5%
h310
 
6.5%
r310
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4752
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1269
26.7%
a642
13.5%
l642
13.5%
M325
 
6.8%
F317
 
6.7%
m317
 
6.7%
O310
 
6.5%
t310
 
6.5%
h310
 
6.5%
r310
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4752
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1269
26.7%
a642
13.5%
l642
13.5%
M325
 
6.8%
F317
 
6.7%
m317
 
6.7%
O310
 
6.5%
t310
 
6.5%
h310
 
6.5%
r310
 
6.5%

region
Categorical

Missing 

Distinct4
Distinct (%)0.4%
Missing52
Missing (%)5.2%
Memory size7.9 KiB
South
261 
North
241 
West
234 
East
212 

Length

Max length5
Median length5
Mean length4.5295359
Min length4

Characters and Unicode

Total characters4294
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth
2nd rowWest
3rd rowSouth
4th rowSouth
5th rowEast

Common Values

ValueCountFrequency (%)
South261
26.1%
North241
24.1%
West234
23.4%
East212
21.2%
(Missing)52
 
5.2%

Length

2026-02-17T16:17:01.291288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-17T16:17:01.399363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
south261
27.5%
north241
25.4%
west234
24.7%
east212
22.4%

Most occurring characters

ValueCountFrequency (%)
t948
22.1%
o502
11.7%
h502
11.7%
s446
10.4%
u261
 
6.1%
S261
 
6.1%
r241
 
5.6%
N241
 
5.6%
W234
 
5.4%
e234
 
5.4%
Other values (2)424
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)4294
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t948
22.1%
o502
11.7%
h502
11.7%
s446
10.4%
u261
 
6.1%
S261
 
6.1%
r241
 
5.6%
N241
 
5.6%
W234
 
5.4%
e234
 
5.4%
Other values (2)424
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4294
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t948
22.1%
o502
11.7%
h502
11.7%
s446
10.4%
u261
 
6.1%
S261
 
6.1%
r241
 
5.6%
N241
 
5.6%
W234
 
5.4%
e234
 
5.4%
Other values (2)424
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4294
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t948
22.1%
o502
11.7%
h502
11.7%
s446
10.4%
u261
 
6.1%
S261
 
6.1%
r241
 
5.6%
N241
 
5.6%
W234
 
5.4%
e234
 
5.4%
Other values (2)424
9.9%

education_level
Categorical

Missing 

Distinct4
Distinct (%)0.4%
Missing51
Missing (%)5.1%
Memory size7.9 KiB
Post-Graduate
266 
Secondary
233 
Graduate
226 
Primary
224 

Length

Max length13
Median length9
Mean length9.4109589
Min length7

Characters and Unicode

Total characters8931
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduate
2nd rowPost-Graduate
3rd rowPrimary
4th rowGraduate
5th rowPost-Graduate

Common Values

ValueCountFrequency (%)
Post-Graduate266
26.6%
Secondary233
23.3%
Graduate226
22.6%
Primary224
22.4%
(Missing)51
 
5.1%

Length

2026-02-17T16:17:01.557728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-17T16:17:01.630108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
post-graduate266
28.0%
secondary233
24.6%
graduate226
23.8%
primary224
23.6%

Most occurring characters

ValueCountFrequency (%)
a1441
16.1%
r1173
13.1%
t758
8.5%
e725
8.1%
d725
8.1%
o499
 
5.6%
u492
 
5.5%
G492
 
5.5%
P490
 
5.5%
y457
 
5.1%
Other values (7)1679
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)8931
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1441
16.1%
r1173
13.1%
t758
8.5%
e725
8.1%
d725
8.1%
o499
 
5.6%
u492
 
5.5%
G492
 
5.5%
P490
 
5.5%
y457
 
5.1%
Other values (7)1679
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8931
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1441
16.1%
r1173
13.1%
t758
8.5%
e725
8.1%
d725
8.1%
o499
 
5.6%
u492
 
5.5%
G492
 
5.5%
P490
 
5.5%
y457
 
5.1%
Other values (7)1679
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8931
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1441
16.1%
r1173
13.1%
t758
8.5%
e725
8.1%
d725
8.1%
o499
 
5.6%
u492
 
5.5%
G492
 
5.5%
P490
 
5.5%
y457
 
5.1%
Other values (7)1679
18.8%

employment_type
Categorical

Missing 

Distinct3
Distinct (%)0.3%
Missing54
Missing (%)5.4%
Memory size7.9 KiB
Salaried
333 
Unemployed
322 
Self-Employed
291 

Length

Max length13
Median length10
Mean length10.218816
Min length8

Characters and Unicode

Total characters9667
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnemployed
2nd rowSelf-Employed
3rd rowUnemployed
4th rowSalaried
5th rowSalaried

Common Values

ValueCountFrequency (%)
Salaried333
33.3%
Unemployed322
32.2%
Self-Employed291
29.1%
(Missing)54
 
5.4%

Length

2026-02-17T16:17:01.759987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-17T16:17:01.823163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
salaried333
35.2%
unemployed322
34.0%
self-employed291
30.8%

Most occurring characters

ValueCountFrequency (%)
e1559
16.1%
l1237
12.8%
d946
9.8%
a666
 
6.9%
S624
 
6.5%
o613
 
6.3%
p613
 
6.3%
m613
 
6.3%
y613
 
6.3%
r333
 
3.4%
Other values (6)1850
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)9667
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1559
16.1%
l1237
12.8%
d946
9.8%
a666
 
6.9%
S624
 
6.5%
o613
 
6.3%
p613
 
6.3%
m613
 
6.3%
y613
 
6.3%
r333
 
3.4%
Other values (6)1850
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9667
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1559
16.1%
l1237
12.8%
d946
9.8%
a666
 
6.9%
S624
 
6.5%
o613
 
6.3%
p613
 
6.3%
m613
 
6.3%
y613
 
6.3%
r333
 
3.4%
Other values (6)1850
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9667
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1559
16.1%
l1237
12.8%
d946
9.8%
a666
 
6.9%
S624
 
6.5%
o613
 
6.3%
p613
 
6.3%
m613
 
6.3%
y613
 
6.3%
r333
 
3.4%
Other values (6)1850
19.1%

annual_income
Real number (ℝ)

Missing 

Distinct950
Distinct (%)100.0%
Missing50
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean598976.8
Minimum-64951.14
Maximum1212389.4
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size7.9 KiB
2026-02-17T16:17:01.916262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-64951.14
5-th percentile271672.07
Q1463669.3
median598894.3
Q3737418.18
95-th percentile918892.45
Maximum1212389.4
Range1277340.5
Interquartile range (IQR)273748.87

Descriptive statistics

Standard deviation195456.74
Coefficient of variation (CV)0.32631772
Kurtosis-0.25766435
Mean598976.8
Median Absolute Deviation (MAD)136607.72
Skewness-0.034094904
Sum5.6902796 × 108
Variance3.8203338 × 1010
MonotonicityNot monotonic
2026-02-17T16:17:02.096637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
726722.311
 
0.1%
617593.761
 
0.1%
121745.111
 
0.1%
585024.811
 
0.1%
690836.631
 
0.1%
493053.351
 
0.1%
457501.161
 
0.1%
888884.081
 
0.1%
587942.21
 
0.1%
690959.031
 
0.1%
Other values (940)940
94.0%
(Missing)50
 
5.0%
ValueCountFrequency (%)
-64951.141
0.1%
63042.391
0.1%
67133.541
0.1%
121745.111
0.1%
125949.51
0.1%
133869.051
0.1%
135608.721
0.1%
150371.421
0.1%
152734.191
0.1%
158716.231
0.1%
ValueCountFrequency (%)
1212389.391
0.1%
1153195.921
0.1%
1136934.851
0.1%
1110937.271
0.1%
1082009.731
0.1%
1070041.161
0.1%
1043840.251
0.1%
1022027.121
0.1%
1016153.181
0.1%
1015127.511
0.1%

loan_amount
Real number (ℝ)

Missing 

Distinct956
Distinct (%)100.0%
Missing44
Missing (%)4.4%
Infinite0
Infinite (%)0.0%
Mean298443.54
Minimum9553.56
Maximum564503.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-17T16:17:02.286491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9553.56
5-th percentile137258.1
Q1231770.91
median294768.7
Q3365947.58
95-th percentile463612.47
Maximum564503.68
Range554950.12
Interquartile range (IQR)134176.67

Descriptive statistics

Standard deviation99556.028
Coefficient of variation (CV)0.33358412
Kurtosis-0.14050116
Mean298443.54
Median Absolute Deviation (MAD)66926.095
Skewness0.055598952
Sum2.8531203 × 108
Variance9.9114026 × 109
MonotonicityNot monotonic
2026-02-17T16:17:02.466995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
516400.741
 
0.1%
359873.91
 
0.1%
207976.151
 
0.1%
97458.191
 
0.1%
380892.471
 
0.1%
262581.971
 
0.1%
284443.431
 
0.1%
376619.61
 
0.1%
401041.381
 
0.1%
422371.031
 
0.1%
Other values (946)946
94.6%
(Missing)44
 
4.4%
ValueCountFrequency (%)
9553.561
0.1%
10010.621
0.1%
27772.231
0.1%
28675.741
0.1%
35910.231
0.1%
37293.461
0.1%
37932.91
0.1%
41158.341
0.1%
50150.641
0.1%
62356.711
0.1%
ValueCountFrequency (%)
564503.681
0.1%
563769.491
0.1%
556990.71
0.1%
551153.961
0.1%
550164.51
0.1%
5484971
0.1%
544458.281
0.1%
544150.431
0.1%
541421.731
0.1%
539076.621
0.1%

loan_purpose
Categorical

Missing 

Distinct5
Distinct (%)0.5%
Missing37
Missing (%)3.7%
Memory size7.9 KiB
Car
226 
Other
198 
Education
190 
Home
175 
Business
174 

Length

Max length9
Median length5
Mean length5.6801661
Min length3

Characters and Unicode

Total characters5470
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHome
2nd rowCar
3rd rowHome
4th rowOther
5th rowBusiness

Common Values

ValueCountFrequency (%)
Car226
22.6%
Other198
19.8%
Education190
19.0%
Home175
17.5%
Business174
17.4%
(Missing)37
 
3.7%

Length

2026-02-17T16:17:02.669789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-17T16:17:02.779932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
car226
23.5%
other198
20.6%
education190
19.7%
home175
18.2%
business174
18.1%

Most occurring characters

ValueCountFrequency (%)
e547
 
10.0%
s522
 
9.5%
r424
 
7.8%
a416
 
7.6%
t388
 
7.1%
o365
 
6.7%
i364
 
6.7%
n364
 
6.7%
u364
 
6.7%
C226
 
4.1%
Other values (8)1490
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)5470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e547
 
10.0%
s522
 
9.5%
r424
 
7.8%
a416
 
7.6%
t388
 
7.1%
o365
 
6.7%
i364
 
6.7%
n364
 
6.7%
u364
 
6.7%
C226
 
4.1%
Other values (8)1490
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e547
 
10.0%
s522
 
9.5%
r424
 
7.8%
a416
 
7.6%
t388
 
7.1%
o365
 
6.7%
i364
 
6.7%
n364
 
6.7%
u364
 
6.7%
C226
 
4.1%
Other values (8)1490
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e547
 
10.0%
s522
 
9.5%
r424
 
7.8%
a416
 
7.6%
t388
 
7.1%
o365
 
6.7%
i364
 
6.7%
n364
 
6.7%
u364
 
6.7%
C226
 
4.1%
Other values (8)1490
27.2%

credit_score
Real number (ℝ)

Missing 

Distinct261
Distinct (%)27.3%
Missing45
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean646.82199
Minimum476
Maximum837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-17T16:17:02.929566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum476
5-th percentile545
Q1608.5
median646
Q3687
95-th percentile750
Maximum837
Range361
Interquartile range (IQR)78.5

Descriptive statistics

Standard deviation60.788751
Coefficient of variation (CV)0.09398065
Kurtosis0.1084169
Mean646.82199
Median Absolute Deviation (MAD)39
Skewness0.11856696
Sum617715
Variance3695.2723
MonotonicityNot monotonic
2026-02-17T16:17:03.044065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65512
 
1.2%
65211
 
1.1%
65111
 
1.1%
63911
 
1.1%
64911
 
1.1%
63010
 
1.0%
6199
 
0.9%
6569
 
0.9%
5999
 
0.9%
6389
 
0.9%
Other values (251)853
85.3%
(Missing)45
 
4.5%
ValueCountFrequency (%)
4761
0.1%
4801
0.1%
4861
0.1%
4971
0.1%
4981
0.1%
4991
0.1%
5001
0.1%
5041
0.1%
5061
0.1%
5071
0.1%
ValueCountFrequency (%)
8371
0.1%
8361
0.1%
8262
0.2%
8251
0.1%
8191
0.1%
8121
0.1%
8051
0.1%
8041
0.1%
8021
0.1%
8002
0.2%

repayment_history
Real number (ℝ)

Missing  Zeros 

Distinct13
Distinct (%)1.4%
Missing46
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean6.0880503
Minimum0
Maximum12
Zeros71
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-17T16:17:03.157763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q39
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.7927146
Coefficient of variation (CV)0.62297688
Kurtosis-1.2234114
Mean6.0880503
Median Absolute Deviation (MAD)3
Skewness-0.015684887
Sum5808
Variance14.384684
MonotonicityNot monotonic
2026-02-17T16:17:03.313112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1293
9.3%
886
8.6%
682
8.2%
281
8.1%
975
7.5%
174
7.4%
573
7.3%
372
 
7.2%
071
 
7.1%
1067
 
6.7%
Other values (3)180
18.0%
ValueCountFrequency (%)
071
7.1%
174
7.4%
281
8.1%
372
7.2%
454
5.4%
573
7.3%
682
8.2%
763
6.3%
886
8.6%
975
7.5%
ValueCountFrequency (%)
1293
9.3%
1163
6.3%
1067
6.7%
975
7.5%
886
8.6%
763
6.3%
682
8.2%
573
7.3%
454
5.4%
372
7.2%

transaction_count
Real number (ℝ)

Missing 

Distinct199
Distinct (%)20.9%
Missing49
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean98.44795
Minimum1
Maximum199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-17T16:17:03.476184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q150
median96
Q3147
95-th percentile189
Maximum199
Range198
Interquartile range (IQR)97

Descriptive statistics

Standard deviation57.346749
Coefficient of variation (CV)0.58250832
Kurtosis-1.2035856
Mean98.44795
Median Absolute Deviation (MAD)48
Skewness0.047010588
Sum93624
Variance3288.6497
MonotonicityNot monotonic
2026-02-17T16:17:03.630829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13411
 
1.1%
19211
 
1.1%
18710
 
1.0%
4810
 
1.0%
1819
 
0.9%
159
 
0.9%
868
 
0.8%
748
 
0.8%
1648
 
0.8%
268
 
0.8%
Other values (189)859
85.9%
(Missing)49
 
4.9%
ValueCountFrequency (%)
16
0.6%
24
0.4%
34
0.4%
46
0.6%
53
0.3%
62
 
0.2%
73
0.3%
87
0.7%
95
0.5%
107
0.7%
ValueCountFrequency (%)
1994
 
0.4%
1985
0.5%
1976
0.6%
1962
 
0.2%
1954
 
0.4%
1941
 
0.1%
1934
 
0.4%
19211
1.1%
1914
 
0.4%
1905
0.5%

spending_ratio
Real number (ℝ)

Missing 

Distinct891
Distinct (%)93.7%
Missing49
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean44.554311
Minimum10
Maximum79.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-17T16:17:03.784452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile13.575
Q128.55
median44.55
Q360.84
95-th percentile75.95
Maximum79.9
Range69.9
Interquartile range (IQR)32.29

Descriptive statistics

Standard deviation19.5181
Coefficient of variation (CV)0.43807433
Kurtosis-1.1208868
Mean44.554311
Median Absolute Deviation (MAD)16.17
Skewness0.024878433
Sum42371.15
Variance380.95622
MonotonicityNot monotonic
2026-02-17T16:17:04.002263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.873
 
0.3%
58.673
 
0.3%
54.622
 
0.2%
38.52
 
0.2%
19.382
 
0.2%
43.362
 
0.2%
19.682
 
0.2%
44.732
 
0.2%
36.322
 
0.2%
60.432
 
0.2%
Other values (881)929
92.9%
(Missing)49
 
4.9%
ValueCountFrequency (%)
101
0.1%
10.061
0.1%
10.181
0.1%
10.311
0.1%
10.371
0.1%
10.391
0.1%
10.41
0.1%
10.571
0.1%
10.652
0.2%
10.681
0.1%
ValueCountFrequency (%)
79.91
0.1%
79.891
0.1%
79.791
0.1%
79.51
0.1%
79.441
0.1%
79.421
0.1%
79.221
0.1%
79.051
0.1%
79.022
0.2%
78.791
0.1%

join_date
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.1%
Missing56
Missing (%)5.6%
Memory size7.9 KiB
54:25.3
944 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters6608
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row54:25.3
2nd row54:25.3
3rd row54:25.3
4th row54:25.3
5th row54:25.3

Common Values

ValueCountFrequency (%)
54:25.3944
94.4%
(Missing)56
 
5.6%

Length

2026-02-17T16:17:04.108271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-17T16:17:04.157193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
54:25.3944
100.0%

Most occurring characters

ValueCountFrequency (%)
51888
28.6%
4944
14.3%
:944
14.3%
2944
14.3%
.944
14.3%
3944
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)6608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
51888
28.6%
4944
14.3%
:944
14.3%
2944
14.3%
.944
14.3%
3944
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
51888
28.6%
4944
14.3%
:944
14.3%
2944
14.3%
.944
14.3%
3944
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
51888
28.6%
4944
14.3%
:944
14.3%
2944
14.3%
.944
14.3%
3944
14.3%

default_flag
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
843 
1
157 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0843
84.3%
1157
 
15.7%

Length

2026-02-17T16:17:04.218921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-17T16:17:04.311289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0843
84.3%
1157
 
15.7%

Most occurring characters

ValueCountFrequency (%)
0843
84.3%
1157
 
15.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0843
84.3%
1157
 
15.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0843
84.3%
1157
 
15.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0843
84.3%
1157
 
15.7%

Interactions

2026-02-17T16:16:57.538290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:43.693498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:46.236672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:48.611884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:51.082886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:53.465100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:55.564234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:56.515265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:57.647114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:44.043887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:46.523006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:48.917633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:51.378247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:53.778714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:55.719957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:56.671320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:57.779785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:44.332627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:46.799551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:49.209466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:51.648431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:54.088850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:55.862808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:56.777729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:57.918050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:44.656507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:47.108039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:49.539645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:51.945940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:54.410301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:55.955762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:56.910409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:58.020677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:44.962547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:47.403415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:49.854694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:52.236317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:54.735357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:56.041543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:57.035463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:58.122579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:45.273741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:47.723758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:50.152656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:52.554225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:55.063304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:56.141953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:57.190466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:58.260682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:45.592771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:48.012783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:50.448113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:52.847410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:55.285277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:56.283667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:57.315691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:58.436087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:45.925602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:48.323914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:50.763552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:53.158628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:55.423881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:56.416991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T16:16:57.430926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-17T16:17:04.411329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ageannual_incomecredit_scorecustomer_iddefault_flageducation_levelemployment_typegenderloan_amountloan_purposeregionrepayment_historyspending_ratiotransaction_count
age1.000-0.0220.022-0.0130.0280.0000.0720.000-0.0820.0000.0000.0550.0070.036
annual_income-0.0221.000-0.014-0.0160.0760.0000.0000.000-0.0090.0250.046-0.010-0.0230.082
credit_score0.022-0.0141.000-0.0610.0510.0000.0000.0000.0580.0000.0000.0280.0260.009
customer_id-0.013-0.016-0.0611.0000.0510.0000.0460.000-0.0230.0000.0000.022-0.035-0.047
default_flag0.0280.0760.0510.0511.0000.0000.0000.0000.0000.0000.0370.0000.0000.111
education_level0.0000.0000.0000.0000.0001.0000.0000.0000.0150.0350.0000.0830.0000.011
employment_type0.0720.0000.0000.0460.0000.0001.0000.0000.0000.0560.0000.0000.0000.000
gender0.0000.0000.0000.0000.0000.0000.0001.0000.0480.0000.0440.0000.0000.096
loan_amount-0.082-0.0090.058-0.0230.0000.0150.0000.0481.0000.0000.0320.029-0.0110.021
loan_purpose0.0000.0250.0000.0000.0000.0350.0560.0000.0001.0000.0510.0000.0000.000
region0.0000.0460.0000.0000.0370.0000.0000.0440.0320.0511.0000.0510.0360.012
repayment_history0.055-0.0100.0280.0220.0000.0830.0000.0000.0290.0000.0511.000-0.022-0.019
spending_ratio0.007-0.0230.026-0.0350.0000.0000.0000.000-0.0110.0000.036-0.0221.000-0.046
transaction_count0.0360.0820.009-0.0470.1110.0110.0000.0960.0210.0000.012-0.019-0.0461.000

Missing values

2026-02-17T16:16:58.700286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-17T16:16:58.961129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-02-17T16:16:59.557867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

customer_idagegenderregioneducation_levelemployment_typeannual_incomeloan_amountloan_purposecredit_scorerepayment_historytransaction_countspending_ratiojoin_datedefault_flag
0100156.0OtherNorthGraduateUnemployed763214.57214669.67Home742.09.0121.078.1654:25.30
1100269.0OtherWestPost-GraduateSelf-Employed585157.80308528.42Car717.08.061.026.1454:25.31
2100346.0OtherSouthPrimaryUnemployed817492.83418049.09Home622.02.0100.064.1054:25.30
3100432.0MaleSouthGraduateSalaried784832.36527840.20Other683.011.051.033.7354:25.30
4100560.0OtherEastNaNSalaried515473.29365736.50BusinessNaN2.098.020.0254:25.30
5100625.0MaleSouthPost-GraduateSalaried735319.60172959.13Car724.09.0137.070.0554:25.30
6100738.0FemaleSouthPost-GraduateSalaried492808.17497086.43Business709.08.02.031.6254:25.30
71008NaNOtherEastPrimarySelf-EmployedNaN298053.13Business566.09.05.056.1254:25.31
8100936.0MaleSouthGraduateSalaried340176.67216229.06Education685.02.0190.0NaN54:25.30
9101040.0MaleSouthPrimarySelf-Employed355557.64192060.75Education638.07.0197.043.0254:25.30
customer_idagegenderregioneducation_levelemployment_typeannual_incomeloan_amountloan_purposecredit_scorerepayment_historytransaction_countspending_ratiojoin_datedefault_flag
990199124.0FemaleEastGraduateSalaried749006.11186282.12Home689.04.0145.054.38NaN0
991199220.0FemaleSouthPost-GraduateUnemployed887889.14210717.04Business655.04.0NaN63.2654:25.30
992199364.0MaleWestGraduateSalaried508090.21453293.34Business545.010.059.079.0254:25.30
993199440.0FemaleNaNPost-GraduateNaN252377.67303270.78Home598.06.054.022.6354:25.30
9941995NaNMaleSouthSecondarySalaried809050.20310168.54Education690.0NaN86.063.6254:25.30
995199660.0MaleWestPrimarySalaried669953.90310565.38Education660.03.012.033.7254:25.30
996199764.0MaleNorthPrimaryUnemployed540790.78439462.54Car721.08.0192.022.2954:25.30
997199862.0MaleEastPrimarySelf-Employed551969.44195478.56Business674.012.06.039.5054:25.30
998199935.0FemaleNorthPrimaryUnemployed1110937.27260360.45Education592.010.053.065.5754:25.30
999200055.0MaleWestPrimaryUnemployed471561.07283103.48Education602.09.0144.042.5454:25.30